Dependency of ray tune about ultralytics

[github-actions]

👋 Hello @jjerry-k, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

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[glenn-jocher]

@jjerry-k hi Jerry,

Thank you for your kind words and for bringing this to our attention! 😊

To help us investigate the issue more effectively, could you please provide a minimal reproducible example? This will allow us to replicate the problem on our end. You can find guidelines on how to create one here: Minimum Reproducible Example.

Additionally, please ensure that you are using the latest versions of torch and ultralytics. You can upgrade your packages using the following commands:

pip install --upgrade torch ultralytics

Regarding the dependency issue with numpy 2.0, it might be causing conflicts with other packages. As a temporary workaround, you can try specifying an earlier version of numpy that is compatible with your other dependencies. For example:

pip install numpy==1.21.0

Then, proceed with installing ray[tune]:

pip install "ray[tune]"

If the issue persists after these steps, please share the specific error messages or conflicts you are encountering. This information will be invaluable in diagnosing and resolving the problem.

Looking forward to your response!